import numpy as np

conv2 = np.array([118.75,155.5,197.75,238.75,239.5,228,303,629,789,599.75,499.25,527.25,477.75,260.5,180.75,296.75,263.5,155.75,108.5,199.5,379.75,109.5,74.25,56.75,-95,-93,-29,90.5,302,56.5,126.75,np.nan])

conv1 = np.array([np.nan,118.75,155.5,197.75,238.75,239.5,228,303,629,789,599.75,499.25,527.25,477.75,260.5,180.75,296.75,263.5,155.75,108.5,199.5,379.75,109.5,74.25,56.75,-95,-93,-29,90.5,302,56.5,126.75])

recurse = np.array([-50,137.5,239.625,428.09375,627.9765625,815.005859375,993.248535156,1277.68786621,1935.57803345,2580.10549164,2948.97362709,3345.75659323,3786.56085169,4133.35978708,4241.66005323,4390.58498669,4690.35375333,4854.41156167,4941.39710958,5021.6507226,5233.58731935,5609.60317016,5518.59920746,5639.35019814,5652.16245047,5507.95938738,5467.01015315,5464.24746171,5589.93813457,5919.51546636,5792.12113341,6007.96971665])

recurse_init = np.array([87.5,278.125,379.46875,568.1328125,767.966796875,955.008300781,1133.2479248,1417.6880188,2075.5779953,2720.10550117,3088.97362471,3485.75659382,3926.56085154,4273.35978711,4381.66005322,4530.58498669,4830.35375333,4994.41156167,5081.39710958,5161.6507226,5373.58731935,5749.60317016,5658.59920746,5779.35019814,5792.16245047,5647.95938738,5607.01015315,5604.24746171,5729.93813457,6059.51546636,5932.12113341,6147.96971665])

conv2_na = np.array([118.75,155.5,197.75,238.75,239.5,228,303,629,np.nan,np.nan,499.25,527.25,477.75,260.5,180.75,296.75,263.5,155.75,108.5,199.5,379.75,109.5,74.25,56.75,-95,-93,-29,90.5,302,56.5,126.75,np.nan])

conv1_na = np.array([np.nan,118.75,155.5,197.75,238.75,239.5,228,303,629,np.nan,np.nan,499.25,527.25,477.75,260.5,180.75,296.75,263.5,155.75,108.5,199.5,379.75,109.5,74.25,56.75,-95,-93,-29,90.5,302,56.5,126.75])

recurse_na = np.array([-50,137.5,239.625,428.09375,627.9765625,815.005859375,993.248535156,1277.68786621,1935.57803345,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan])

recurse_init_na = np.array([87.5,278.125,379.46875,568.1328125,767.966796875,955.008300781,1133.2479248,1417.6880188,2075.5779953,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan])

conv2_odd = np.array([np.nan,np.nan,366.95,412.75,419.05,501.15,850.85,1138.65,1109,1053.15,1043.2,946.35,687.55,523.5,544.65,485.25,371.3,297.2,344.9,517.85,319.55,260.3,191.15,-11.35,-95.45,-72.15,40.25,299.85,173.95,247.5,np.nan,np.nan])

conv1_odd = np.array([np.nan,np.nan,np.nan,np.nan,366.95,412.75,419.05,501.15,850.85,1138.65,1109,1053.15,1043.2,946.35,687.55,523.5,544.65,485.25,371.3,297.2,344.9,517.85,319.55,260.3,191.15,-11.35,-95.45,-72.15,40.25,299.85,173.95,247.5])

recurse_odd = np.array([191.5,462.125,668.84375,1044.1453125,1556.46835938,2238.65205078,3175.44806152,4572.56601685,6833.45236176,9776.38394429,13509.7387615,18791.5897613,26145.4239591,36153.4065035,49699.8299323,68480.4947171,94501.551723,130110.583827,179061.168784,246469.715955,339396.406323,467401.785292,643016.749056,885080.436404,1218108.49028,1676305.60832,2307074.11064,3175195.69641,4370080.25182,6014713.24095,8277634.14851,11392536.8578])
